Python 3.11.3 (v3.11.3:f3909b8bc8, Apr 4 2023, 20:12:10) [Clang 13.0.0 (clang-1300.0.29.30)]
Type 'copyright', 'credits' or 'license' for more information
IPython 8.26.0 -- An enhanced Interactive Python. Type '?' for help.

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
from IPython.display import display

df = pd.read_pickle("../../data/interim/processed_data_01.pkl")

set_df = df[df["set"] == 1]
plt.plot(set_df["acc_y"].reset_index(drop = True))

[<matplotlib.lines.Line2D at 0x137b29710>]

for label in df["label"].unique():
    subset = df[df["label"] == label]
    fig, ax = plt.subplots()
    plt.plot(subset["acc_y"].reset_index(drop = True), label = label)
    plt.legend()
    plt.show()

for label in df["label"].unique():
    subset = df[df["label"] == label]
    fig, ax = plt.subplots()
    plt.plot(subset[:100]["acc_y"].reset_index(drop = True), label = label)
    plt.legend()
    plt.show()

print(plt.style.available)
mpl.style.use("seaborn-v0_8-deep")
mpl.rcParams["figure.figsize"] = (20, 5)
mpl.rcParams["figure.dpi"] = 100

['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-v0_8', 'seaborn-v0_8-bright', 'seaborn-v0_8-colorblind', 'seaborn-v0_8-dark', 'seaborn-v0_8-dark-palette', 'seaborn-v0_8-darkgrid', 'seaborn-v0_8-deep', 'seaborn-v0_8-muted', 'seaborn-v0_8-notebook', 'seaborn-v0_8-paper', 'seaborn-v0_8-pastel', 'seaborn-v0_8-poster', 'seaborn-v0_8-talk', 'seaborn-v0_8-ticks', 'seaborn-v0_8-white', 'seaborn-v0_8-whitegrid', 'tableau-colorblind10']

category_df = df.query("label == 'squat'").query("participant == 'A'").reset_index()

fig, ax = plt.subplots()
category_df.groupby(["category"])["acc_y"].plot()
ax.set_xlabel("samples")
ax.set_ylabel("acc_y")
plt.legend()

<matplotlib.legend.Legend at 0x137fa3210>

participant_df = df.query("label == 'bench'").sort_values("participant").reset_index()
fig, ax = plt.subplots()
participant_df.groupby(["participant"])["acc_y"].plot()
ax.set_xlabel("samples")
ax.set_ylabel("acc_y")
plt.legend()

<matplotlib.legend.Legend at 0x137d1a850>

label = "squat"
participant = "A"
all_axis_df = df.query(f"label == '{label}'").query(f"participant == '{participant}'")

fig, ax = plt.subplots()
all_axis_df[["acc_x", "acc_y", "acc_z"]].plot(ax=ax)
ax.set_xlabel("samples")
ax.set_ylabel("acc_y")
plt.legend()

<matplotlib.legend.Legend at 0x1400c4890>

label = "squat"
participant = "A"
all_axis_df = df.query(f"label == '{label}'").query(f"participant == '{participant}'")
fig, ax = plt.subplots()
all_axis_df[["acc_x", "acc_y", "acc_z"]].plot()
ax.set_ylabel("acc_y")
ax.set_xlabel("samples")
plt.legend()

<matplotlib.legend.Legend at 0x1401ded90>

fig, ax2 = plt.subplots()
all_axis_df[["acc_x", "acc_y", "acc_z"]].plot(ax = ax2)
ax.set_ylabel("acc_y")
ax.set_xlabel("samples")
plt.legend()

<matplotlib.legend.Legend at 0x1402c4d50>

df = pd.read_pickle("../../data/interim/processed_data_01.pkl")

df

	acc_x	acc_y	acc_z	gyr_x	gyr_y	gyr_z	label	category	participant	set
epoch (ms)
2019-01-11 15:08:05.200	0.013500	0.977000	-0.071000	-1.8904	2.4392	0.9388	bench	heavy	B	64
2019-01-11 15:08:05.400	-0.001500	0.970500	-0.079500	-1.6826	-0.8904	2.1708	bench	heavy	B	64
2019-01-11 15:08:05.600	0.001333	0.971667	-0.064333	2.5608	-0.2560	-1.4146	bench	heavy	B	64
2019-01-11 15:08:05.800	-0.024000	0.957000	-0.073500	8.0610	-4.5244	-2.0730	bench	heavy	B	64
2019-01-11 15:08:06.000	-0.028000	0.957667	-0.115000	2.4390	-1.5486	-3.6098	bench	heavy	B	64
...	...	...	...	...	...	...	...	...	...	...
2019-01-20 17:33:27.000	-0.048000	-1.041500	-0.076500	1.4146	-5.6218	0.2926	row	medium	E	71
2019-01-20 17:33:27.200	-0.037000	-1.030333	-0.053333	-2.7684	-0.5854	2.2440	row	medium	E	71
2019-01-20 17:33:27.400	-0.060000	-1.031000	-0.082000	2.8416	-5.1342	-0.1220	row	medium	E	71
2019-01-20 17:33:27.600	-0.038667	-1.025667	-0.044667	-0.2318	0.2562	1.1220	row	medium	E	71
2019-01-20 17:33:27.800	-0.044000	-1.034000	-0.059000	1.0980	-4.0240	0.9760	row	medium	E	71

9009 rows × 10 columns

df.columns

Index(['acc_x', 'acc_y', 'acc_z', 'gyr_x', 'gyr_y', 'gyr_z', 'label',
       'category', 'participant', 'set'],
      dtype='object')

df = df[['acc_x', 'acc_y', 'acc_z', 'gyr_x', 'gyr_y', 'gyr_z', 'participant', 'label', 'category', 'set']]

df = df[['acc_x', 'acc_y', 'acc_z', 'gyr_x', 'gyr_y', 'gyr_z', 'participant', 'label', 'category', 'set']]

df

	acc_x	acc_y	acc_z	gyr_x	gyr_y	gyr_z	participant	label	category	set
epoch (ms)
2019-01-11 15:08:05.200	0.013500	0.977000	-0.071000	-1.8904	2.4392	0.9388	B	bench	heavy	64
2019-01-11 15:08:05.400	-0.001500	0.970500	-0.079500	-1.6826	-0.8904	2.1708	B	bench	heavy	64
2019-01-11 15:08:05.600	0.001333	0.971667	-0.064333	2.5608	-0.2560	-1.4146	B	bench	heavy	64
2019-01-11 15:08:05.800	-0.024000	0.957000	-0.073500	8.0610	-4.5244	-2.0730	B	bench	heavy	64
2019-01-11 15:08:06.000	-0.028000	0.957667	-0.115000	2.4390	-1.5486	-3.6098	B	bench	heavy	64
...	...	...	...	...	...	...	...	...	...	...
2019-01-20 17:33:27.000	-0.048000	-1.041500	-0.076500	1.4146	-5.6218	0.2926	E	row	medium	71
2019-01-20 17:33:27.200	-0.037000	-1.030333	-0.053333	-2.7684	-0.5854	2.2440	E	row	medium	71
2019-01-20 17:33:27.400	-0.060000	-1.031000	-0.082000	2.8416	-5.1342	-0.1220	E	row	medium	71
2019-01-20 17:33:27.600	-0.038667	-1.025667	-0.044667	-0.2318	0.2562	1.1220	E	row	medium	71
2019-01-20 17:33:27.800	-0.044000	-1.034000	-0.059000	1.0980	-4.0240	0.9760	E	row	medium	71

9009 rows × 10 columns

set_df = df[df["set"] == 1]
plt.plot(set_df["acc_y"].reset_index(drop = True))

[<matplotlib.lines.Line2D at 0x14036ae10>]

for label in df["label"].unique():
    subset = df[df["label"] == label]
    fig, ax = plt.subplots()
    plt.plot(subset["acc_y"].reset_index(drop = True), label = label)
    plt.legend()
    plt.show()

for label in df["label"].unique():
    subset = df[df["label"] == label]
    fig, ax = plt.subplots()
    plt.plot(subset[:100]["acc_y"].reset_index(drop = True), label = label)
    plt.legend()
    plt.show()

mpl.style.use("seaborn-v0_8-deep")
mpl.rcParams["figure.figsize"] = (20, 5)
mpl.rcParams["figure.dpi"] = 100

category_df = df.query("label == 'squat'").query("participant == 'A'").reset_index()

fig, ax = plt.subplots()
category_df.groupby(["category"])["acc_y"].plot()
ax.set_xlabel("samples")
ax.set_ylabel("acc_y")
plt.legend()

<matplotlib.legend.Legend at 0x137bfcd50>

participant_df = df.query("label == 'bench'").sort_values("participant").reset_index()
fig, ax = plt.subplots()
participant_df.groupby(["participant"])["acc_y"].plot()
ax.set_xlabel("samples")
ax.set_ylabel("acc_y")
plt.legend()

<matplotlib.legend.Legend at 0x140426710>

label = "squat"
participant = "A"
all_axis_df = df.query(f"label == '{label}'").query(f"participant == '{participant}'")
fig, ax2 = plt.subplots()
all_axis_df[["acc_x", "acc_y", "acc_z"]].plot(ax = ax2)
ax.set_ylabel("acc_y")
ax.set_xlabel("samples")
plt.legend()

<matplotlib.legend.Legend at 0x1404d7510>

labels = df["label"].unique()
participants = df["participant"].unique()

for label in labels:
    for participant in participants:
        all_axis_df = (
            df.query(f"label == '{label}'")
            .query(f"participant == '{participant}'")
            .reset_index()
        )
    fig, ax = plt.subplots()
    all_axis_df[["acc_x", "acc_y", "acc_z"]].plot(ax = ax)
    ax.set_ylabel("acc_y")
    ax.set_xlabel("samples")
    plt.title(f"{label} ({participant})".title())
    plt.legend()

for label in labels:
    for participant in participants:
        all_axis_df = (
            df.query(f"label == '{label}'")
            .query(f"participant == '{participant}'")
            .reset_index()
        )
        if len(all_axis_df) > 0:
            fig, ax = plt.subplots()
            all_axis_df[["acc_x", "acc_y", "acc_z"]].plot(ax = ax)
            ax.set_ylabel("acc_y")
            ax.set_xlabel("samples")
            plt.title(f"{label} ({participant})".title())
            plt.legend()

<ipython-input-30-013967daef1c>:9: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.
  fig, ax = plt.subplots()

for label in labels:
    for participant in participants:
        all_axis_df = (
            df.query(f"label == '{label}'")
            .query(f"participant == '{participant}'")
            .reset_index()
        )
        if len(all_axis_df) > 0:
            fig, ax = plt.subplots()
            all_axis_df[["gyr_x", "gyr_y", "gyr_z"]].plot(ax = ax)
            ax.set_ylabel("gyr_y")
            ax.set_xlabel("samples")
            plt.title(f"{label} ({participant})".title())
            plt.legend()

<ipython-input-31-9f970ca87cf7>:9: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.
  fig, ax = plt.subplots()

label = "row"
participant = "A"
all_axis_df = df.query(f"label == '{label}'").query(f"participant == '{participant}'")
fig, ax = plt.subplots()
all_axis_df[["acc_x", "acc_y", "acc_z"]].plot(ax = ax)
ax.set_ylabel("acc_y")
ax.set_xlabel("samples")
plt.legend()

<matplotlib.legend.Legend at 0x137d39290>

label = "row"
participant = "A"
df_combine = (
    df.query(f"label == '{label}'")
    .query(f"participant == '{participant}'")
    .reset_index(drop = True)
)

df_combine

	acc_x	acc_y	acc_z	gyr_x	gyr_y	gyr_z	participant	label	category	set
0	0.109000	-0.935667	-0.092333	5.24380	-3.82920	2.2806	A	row	heavy	81
1	0.129000	-1.174000	-0.114500	13.47560	-3.81700	4.6462	A	row	heavy	81
2	0.136333	-1.295000	-0.118667	27.29260	-7.97560	-5.4148	A	row	heavy	81
3	0.171000	-1.221500	0.049000	21.90240	-3.80500	-15.5244	A	row	heavy	81
4	0.148000	-0.871333	0.136000	19.07340	-3.59760	1.8050	A	row	heavy	81
...	...	...	...	...	...	...	...	...	...	...
80	0.178333	-0.947000	0.225333	-30.34160	14.63400	14.1586	A	row	heavy	84
81	0.126500	-1.156000	0.160500	-30.25620	3.69500	23.0004	A	row	heavy	84
82	0.047000	-1.204000	0.025000	-20.51220	6.54860	5.0244	A	row	heavy	84
83	0.034000	-1.071000	-0.027000	0.34140	-2.25600	0.4514	A	row	heavy	84
84	0.037000	-1.022500	-0.004500	-1.20425	-3.21625	1.1585	A	row	heavy	84

85 rows × 10 columns

fig, ax = plt.subplots(nrows = 2, sharex = True, figsize = (20, 10))

label = "row"
participant = "A"
df_combine = (
    df.query(f"label == '{label}'")
    .query(f"participant == '{participant}'")
    .reset_index(drop = True)
)

fig, ax = plt.subplots(nrows = 2, sharex = True, figsize = (20, 10))

fig, ax = plt.subplots(nrows = 2, sharex = True, figsize = (20, 10))

fig, ax

(<Figure size 2000x1000 with 2 Axes>,
 array([<Axes: >, <Axes: >], dtype=object))

fig, ax = plt.subplots(nrows = 2, sharex = True, figsize = (20, 10))
df_combine[["acc_x", "acc_y", "acc_z"]].plot(ax = ax[0])
df_combine[["gyr_x", "gyr_y", "gyr_z"]].plot(ax = ax[1])

<Axes: >

ax[0].legend(loc = "upper center", bbox_to_anchor = (0.5, 1.15), ncol = 3, fancybox = True, shadow = True)
ax[1].legend(loc = "upper center", bbox_to_anchor = (0.5, 1.15), ncol = 3, fancybox = True, shadow = True)
ax[1].set_xlabel("samples")

Text(0.5, 4.4444444444444375, 'samples')

fig, ax = plt.subplots(nrows = 2, sharex = True, figsize = (20, 10))
df_combine[["acc_x", "acc_y", "acc_z"]].plot(ax = ax[0])
df_combine[["gyr_x", "gyr_y", "gyr_z"]].plot(ax = ax[1])
#add some styling to the plots

ax[0].legend(loc = "upper center", bbox_to_anchor = (0.5, 1.15), ncol = 3, fancybox = True, shadow = True)
ax[1].legend(loc = "upper center", bbox_to_anchor = (0.5, 1.15), ncol = 3, fancybox = True, shadow = True)
ax[1].set_xlabel("samples")

Text(0.5, 0, 'samples')

labels = df["label"].unique()
participants = df["participant"].unique()
for label in labels:
    for participant in participants:
        df_combine = (
            df.query(f"label == '{label}'")
            .query(f"participant == '{participant}'")
            .reset_index()
        )
        if len(df_combine) > 0:
            fig, ax = plt.subplots(nrows = 2, sharex = True, figsize = (20, 10))
            df_combine[["acc_x", "acc_y", "acc_z"]].plot(ax = ax[0])
            df_combine[["gyr_x", "gyr_y", "gyr_z"]].plot(ax = ax[1])
            #add some styling to the plots
            ax[0].legend(loc = "upper center", bbox_to_anchor = (0.5, 1.15), ncol = 3, fancybox = True, shadow = True)
            ax[1].legend(loc = "upper center", bbox_to_anchor = (0.5, 1.15), ncol = 3, fancybox = True, shadow = True)
            ax[1].set_xlabel("samples")

<ipython-input-43-56f9c59c25b5>:11: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.
  fig, ax = plt.subplots(nrows = 2, sharex = True, figsize = (20, 10))

for label in labels:
    for participant in participants:
        df_combine = (
            df.query(f"label == '{label}'")
            .query(f"participant == '{participant}'")
            .reset_index()
        )
        if len(df_combine) > 0:
            fig, ax = plt.subplots(nrows = 2, sharex = True, figsize = (20, 10))
            df_combine[["acc_x", "acc_y", "acc_z"]].plot(ax = ax[0])
            df_combine[["gyr_x", "gyr_y", "gyr_z"]].plot(ax = ax[1])
            #add some styling to the plots
            ax[0].legend(loc = "upper center", bbox_to_anchor = (0.5, 1.15), ncol = 3, fancybox = True, shadow = True)
            ax[1].legend(loc = "upper center", bbox_to_anchor = (0.5, 1.15), ncol = 3, fancybox = True, shadow = True)
            ax[1].set_xlabel("samples")
            plt.savefig(f"../../reports/figures/{label.title()} ({participant}).png")
            plt.show()